Study on extracting large paddy areas for the low spatial resolution satellite data
Environmental Remote Sensing Laboratory
1. Introduction
Rice is a staple food for half of the world's
population. Rice production revenue has been amain income of many countries on the world. Inagriculture, monitoring of rice plant characteristics
and paddy land information through rice plantdevelopment stages is very valuable in agricultural
management, planning and decision making toensure the long-term sustainability of agriculture.
Nowadays, remote sensing has played asignificant role in studying agriculture. One of theapplications of remote sensing is to identify paddy
rice fields and to extract paddy land information. To
properly use satellite Earth observation for large
paddy areas monitoring, high temporal revisit
frequency over vast geographic areas is necessary.
However, this often limits the spatial resolution.
Therefore, finding the method to extract large paddy
areas using low spatial resolution satellite data isvery useful in present.
2. Research purpose
In order to monitor rice plant characteristics andpaddy land information, it is necessary to extract
paddy area and use the high temporal resolution
satellite data. In Japan, since average size of paddy
field is 30 meter by 100 meter, it is difficult toextract paddy area using the high temporal
resolution but low spatial resolution satellite datasuch as MODIS data (250 meter spatial resolution).
Because of this reason, we proposed extract large
paddy area throughout the spatial purity. Spatial
purity is the ratio of the number of Landsat paddy
pixels in one MODIS pixel.
𝐍𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐩𝐮𝐫𝐢𝐭𝐲 ＝ 𝐋𝐚𝐧𝐝𝐬𝐚𝐭 × 𝟏𝟎𝟎(1)

Le Quynh Mai

use of spatial purity for the low spatial resolution
satellite data.
3. Target region and study data
This research collected Landsat 8 OLI images on
May 25th and July 12th 2015 to extract paddy area andcalculate spatial purity. In this research, we used two
bands of Landsat 8 OLI: red band (0.64 - 0.67 µm)
and NIR band (0.85 - 0.88 µm) with 30-meter spatialresolution. Furthermore, MODIS Terra/Aqua Surface
Reflectance Daily L2G Global 250m (MOD09GQ)
was collected on May 25th and July 12th 2015 withtwo bands: red band (0.620 - 0.670 µm) and NIRband (0.841 - 0.876 µm).
The target regions are all paddy fields of Niigata
prefecture (except for Murakami city andAwashimaura village).
4. Research methodology
Figure 1 shows the process of this research.
Paddy field is a region that its surface conditionchanges dramatically by each growing period of rice
plant. In transplanting period, rice plants are verysmall and paddy fields are covered by water.
Therefore, NIR reflectance at that time is very low. In
growing period, rice plants cover paddy fields. NIR
reflectance in present is higher than NIR reflectanceand red reflectance is less than red reflectance intransplanting period. Based on these characteristics,
we proposed the rice-cropping index (RCI) to extract
vegetation pixels by equation (2):
𝐍𝐈𝐑𝟐!𝐑𝟐!𝐍𝐈𝐑𝟏
𝐑𝐂𝐈＝(2)
𝐍𝐈𝐑𝟐!𝐑𝟐!𝐍𝐈𝐑𝟏

NIR1: near infrared reflectance at transplanting
period.
NIR2 and R2: near infrared reflectance and redreflectance at growing period respectively;
Based on field observation data, we chose thethreshold for extracting vegetation pixels is greaterthan 0.0.
Paddy area is also flat plain region. The flat plain
region is the region that its slope is less than 5 degree.

𝐍𝐌𝐎𝐃𝐈𝐒NLandsat: the number of Landsat paddy pixels in
one MODIS pixel;
NMODIS: the size of one MODIS pixel.
This research’s objective is evaluating theability of extracting large paddy areas through the1In order to extract flat plain pixel, we used the digital
elevation model (DEM) 50m mesh data. The region
where is both vegetation region and flat plain regionis paddy region.
After that, spatial purity is determined. Based on
spatial purity, we extracted large paddy areas forMODIS data. Finally, we evaluated the ability ofextracting large paddy areas for MODIS data using
spatial purity map.
Landsat data
Transplanting period

Growing period

Vegetation pixel

of purity value. For example: If we choose thethreshold of spatial purity is greater than 50%, we canextract 37.2% pixels of total; If we choose thethreshold of spatial purity is greater than 60%, 70%
or 80%, we can extract 30%, 23% or 15.6% pixels oftotal, respectively.
5.2 Assessment the result of paddy areas
extraction using Landsat 8 OLI data
In order to assess the result of extracting paddy
areas from Landsat 8 OLI data, we compared
Landsat paddy area with paddy area from statistical
data of 2014.
Based on the different area percentage between
Landsat paddy area and statistical paddy area, wedivided the cities in to 6 groups as follows:- Group 1 (different area percentage aroundwithin ±10%) includes Niigata city, Tsubame city,
Agano city, Yahiko city, Tagami city and Seiro city.
- Group 2 (different area percentage from -11%
to -20%) includes Mitsuke city, Sanjo city and Kamocity.
- Group 3 (different area percentage from -21%
to -30%) includes Shibata city, Nagaoka city, Kariwa
city and Gosen city.
- Group 4 (different area percentage from -31%
to -40%) includes Tainai city.
- Group 5 (different area percentage from -41%
to -50%) includes Joetsu city and Kashiwazaki city.
- Group 6 (different area percentage greater than-50%) includes remaining cities of target region.
Note that negative value of different area
percentage means Landsat paddy area is smaller thanstatistical paddy area. This result shows that since
the moderate spatial resolution, Landsat cannot
extract all paddy areas, especially mountainous
paddy areas.
5.3 Extracting large paddy areas for MODIS data
To extract MODIS large paddy areas, theimportant thing is finding the suitable threshold of
spatial purity. From the result of section 5.2, wethink that the extraction result will be change when
we choose different spatial purity thresholds forDEM
Slope
Flat plainpixel

Landsat paddy pixel
Spatial purity
Assessment the result of paddy areas extraction using Landsat data

Evaluating the ability of extracting large paddy areas for MODIS
data using spatial purity mapFigure 1. Research Flowchart
5. Research result
5.1 Spatial purity map

Figure 2. Spatial purity map
The spatial purity map (figure 2) shows thenumber of Landsat paddy pixels in MODIS pixels.
The result shows that the number of pixels that has
purity greater than 0 and less than or equal to 10 isbiggest that accounts for 30% pixels of total. The
number of pixels tends to decrease with the increase2

different regions. Therefore, we conducted to extract
large paddy areas for the entire target region and foreach city of target region. For choosing threshold of
spatial purity, we based on two criteria:- Extracted MODIS paddy area is similar withstatistical paddy area.
- Spectral reflectance (mean DVI value) of
MODIS paddy area is similar with Landsat paddy
area. According to the liner mixture model, thereflectance of the mixed pixel is written by equation(3):
𝐑 𝐦 = 𝐀 𝟏 ×𝐑 𝟏 + 𝐀 𝟐 ×𝐑 𝟐 (3)Rm: mean DVI value of the simulated MODIS
pixel (include Landsat paddy and non paddy area).
R1, R2: mean DVI value of Landsat paddy areaand non-paddy area.
A1, A2: abundance of paddy and non-paddyarea.
If mean DVI value of MODIS paddy region andmixed region in the same spatial purity is similar,
the result of research is good. Then, we will choose
this spatial purity if the spectral reflectance of
extracted paddy area is same as spectral reflectanceof Landsat paddy area.
5.3.1 Extracting large paddy areas for theentire target region

From purity greater than 60%, there is a strong
difference between MODIS paddy area andstatistical paddy area.

Figure 4. Mean DVI value of the entire target region
on May 25th
Next, we examined mean DVI value of MODIS
paddy area on May 25th (figure 4) and July 12th(figure 5). From figure 4, we found that mean DVI
value is quite different between MODIS andsimulated MODIS in the same spatial purity. To
identify the reason, we examined MODIS May 25th
image and found that its quality is not good. We
continued to examine MODIS May 26th image andits quality is quite good. Then, we determined DVIand NDVI value of MODIS May 26th with spatial
purity 100%, OLI data and mixed pixel. The result(table 1) shows that compared with MODIS May
25th, mean DVI value and mean NDVI value of
MODIS May 26th is similar with OLI paddy region
and mixed region. Then, the reason why there is thedifference between mean DVI of simulated MODIS
area and MODIS paddy area on 25th May is the low
quality of MODIS 25th May.
Table 1. Comparison the mean value of DVI, NDVIof MODIS and Landsat data
DateFigure 3. Comparison between statistical paddy areaand extracted paddy area of the entire target region
Figure 3 shows the comparison between
statistical paddy area and MODIS paddy areas of theentire target region. Based on this result, if thespatial purity is greater than 20% or 30% for thewhole target area, the area of MODIS paddy region(extracted paddy area) is same as statistical paddyarea. The next better threshold is 40% or 50%.

th

May 25th

May 26LandsatMODIS

MODIS paddypaddy

simulated area(Purity 100%)

DVI = 0.034

DVI = 0.034

DVI = 0.065

NDVI = 0.157

NDVI = 0.156

NDVI = 0.432

Red = 0.091

Red = 0.092

Red = 0.043

NIR = 0.125

NIR = 0.126

NIR = 0.108
DVI = 0.036
NDVI = 0.162
Red = 0.093
NIR = 0.129

3The next step is checking mean DVI value on
July 12th. Figure 5 shows that there is a match
between mean DVI value of MODIS and Landsatpaddy area in same purity region, especially in caseof purity >=50%. From above result, we chose thethreshold of spatial purity to extract large paddyareas of the entire target data is greater than or equal
to 50% (figure 6).
Figure 7. Large paddy areas (orange regions) ofNiigata city

Figure 5. Mean DVI value of the entire target region
on July 12th
Figure 8. Large paddy areas (orange regions) ofJoetsu city
6. Conclusion
This research demonstrates that spatial purity iseffective for extracting large paddy areas for the low
spatial resolution satellite data such as MODIS data.
The result presents that this research can extract
65% paddy areas of the entire target region.
However, if we choose seperate threshold of spatial
purity, the accuracy is higher. For example, in caseof Niigata city, we can extract 98% paddy areas
compared with statistical data with the threshold is60%. In case of Joetsu city, with threshold 20%, wecan extract 89% paddy areas compared withstatistical data.
However, for monitoring the information ofpaddy land, it does not need to exploit the entire
paddy areas exactly like statistics. Therefore, in next
step, this research result can be use for monitoring
paddy land information.
This research result may be improved if weselect spatial purity for each separate city in target
region and consider the use of other kind of DEM
for extracting flat plain region.

Figure 6. Large paddy areas (orange regions) of theentire target region
5.3.2 Extracting large paddy areas for thesome cities in target region
In order to improve the accuracy of this research,
we would like to extract large paddy areas for eachseperate city. Due to limited time, we chose to
conduct this step for two cities in target region. They
are Niigata city (different area pecentage is around10%) and Joetsu city (different area percentage isaround 50%). The result shows that the suitable
threshold for Niigata city is 60% and Joetsu city is20%.

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